Not quite Sherlock Holmes: Language model predictions do not reliably differentiate impossible from improbable events

Can language models reliably predict that possible events are more likely than merely improbable ones?By teasing apart possibility, typicality, and contextual relatedness, we show that despite the results of previous work, language models' ability to do this is far from robust.In fact, under certain conditions, all models tested-including Llama 3, Gemma 2, and Mistral NeMo-perform at worse-than-chance level, assigning higher probabilities to impossible sentences such as 'the car was given a parking ticket by the brake' than to merely unlikely sentences such as 'the car was given a parking ticket by the explorer'.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.findings-acl.696
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.00

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article

Not quite Sherlock Holmes: Language model predictions do not reliably differentiate impossible from improbable events

James A. Michaelov, Ben Bergen, Zhe Zhang, R Estacio
Topic Modeling
article

Not quite Sherlock Holmes: Language model predictions do not reliably differentiate impossible from improbable events

James A. Michaelov, Ben Bergen, Zhe Zhang, R Estacio
article en

Abstract

Can language models reliably predict that possible events are more likely than merely improbable ones?By teasing apart possibility, typicality, and contextual relatedness, we show that despite the results of previous work, language models' ability to do this is far from robust.In fact, under certain conditions, all models tested-including Llama 3, Gemma 2, and Mistral NeMo-perform at worse-than-chance level, assigning higher probabilities to impossible sentences such as 'the car was given a parking ticket by the brake' than to merely unlikely sentences such as 'the car was given a parking ticket by the explorer'.

Andrew W. Mellon Foundation, University of California, San Diego
Openalex Percentile: Top 90%
Topic Modeling
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